Using modelled relationships and satellite observations to attribute modelled aerosol biases over biomass burning regions

Biomass burning (BB) is a major source of aerosols that remain the most uncertain components of the global radiative forcing. Current global models have great difficulty matching observed aerosol optical depth (AOD) over BB regions. A common solution to address modelled AOD biases is scaling BB emissions. Using the relationship from an ensemble of aerosol models and satellite observations, we show that the bias in aerosol modelling results primarily from incorrect lifetimes and underestimated mass extinction coefficients. In turn, these biases seem to be related to incorrect precipitation and underestimated particle sizes. We further show that boosting BB emissions to correct AOD biases over the source region causes an overestimation of AOD in the outflow from Africa by 48%, leading to a double warming effect compared with when biases are simultaneously addressed for both aforementioned factors. Such deviations are particularly concerning in a warming future with increasing emissions from fires.


Supplementary Method 1 | Estimating regional AOD and AE
The regional AOD was estimated via a combination of models and raw POLDER data. For AeroCom models with highresolution output (3-hourly or daily), we derived a linear regression between the model average of AOD at all times and grid boxes (regional AOD) and the model average of AOD collocated with POLDER observations (see Supplementary Fig.   10 for the example of Southern Hemisphere Africa). Then, the regional AOD observation was estimated by employing the average of raw POLDER data for the regression (see the dashed lines in Supplementary Fig. 10). The uncertainties in regional AOD are based on the confidence intervals of the predicted values. We found that the predictions had small uncertainties except for AOD in boreal regions (i.e., boreal North America, Eastern Siberia, as shown in Supplementary   Fig. 4), where the uncertainties resulted from the very limited sampling coverage of the POLDER dataset (< 1%). Similarly, we also estimated the regional AE.
To test the robustness of this method, we removed one of the models to estimate regional AOD and AE and repeated it for all the models. The variations in the predicted regional AOD and AE resulting from the exclusion of individual models were very small (< 5%), suggesting that the method was robust and not dependent on the models chosen for the estimations.

Supplementary Method 2 | Uncertainties from individual factors
The following factors were considered regarding the overall uncertainties throughout the analysis: • Retrieval uncertainties for AOD and AE, quantified as 10% and 0.22, respectively, based on a global validation against the AErosol RObotic NETwork (AERONET) dataset, given the very limited data availability of the AERONET dataset over BB regions 1 ; • Uncertainty of the GPCP dataset, set as 9% according to Adler et al 2 ; • Uncertainty of the regional average AOD and AE, quantified based on the regression confidence intervals, as shown in Supplementary Fig. 10; • Uncertainties in predicting constrained lifetime and MEC, estimated using the regression confidence intervals (see Fig.   2 and Supplementary Fig. 1). Note that the confidence intervals based on the model data range do not necessarily reflect true uncertainty. We assumed that the linear regressions presented reasonable approximations of the real relationships.
The individual uncertainty contributions (see Supplementary Fig. 4) showed that the overall uncertainties were dominated by satellite retrieval errors, suggesting that our assumption would not fundamentally alter the results.
• Uncertainties in the background emissions, see Supplementary Table 5.
Individual distributions were developed for each factor based on the above parameters. These individual uncertainties contribute to the overall uncertainties for constrained emissions, lifetime, MEC, and error attribution. The overall uncertainty was calculated via a Monte-Carlo approach by randomly drawing inputs with replacement from the distribution of each involved parameter 100,000 times (see Table 1 and Fig. 3). The uncertainty caused by a single factor was calculated a. Grid structure is shown as latitude × longitude × vertical layer. b. The BB emission sources provide OC emissions and models need to convert to the OA emissions based on their assumptions on OA/OC ratios, which lead to different emission inputs even using same inventories. c. Data are the regional, fire-season averages, with models collocated and compared with POLDER dataset. Biases for models with monthly frequency are not shown given the sampling issues.  43 . It considers the same aerosol components as BBA (OA, BC, and SO 2 ). b. The biogenic emissions are calculated as 15% of the emitted mass of terpenes to indicate the biogenic SOA 39 . c. The sea salt emissions over Amazon, Africa, and Siberia result from the grid boxes that cover both land and ocean areas in original models given the rather coarse resolutions (see Supplementary Table 1). a. The ambient particle size is only modified for calculations of optical properties, wet, and dry deposition. b. The scaling factor of precipitation is directly applied to wet removal.